Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Data sets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
Importing and Exporting Data in Python
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来自 IBM 的课程
Data Analysis with Python
76 个评分
从本节课中
Importing Datasets
与讲师见面
Joseph SantarcangeloPh.D., Data Scientist at IBM
In this video, we'll look at how to read in data using Python's pandas package.
Once we have our data in Python,
then we can perform all the subsequent data analysis procedures we need.
Data acquisition is a process of loading
and reading data into notebook from various sources.
To read any data using Python's pandas package,
there are two important factors to consider,
format and file path.
Format is the way data is encoded.
We can usually tell different encoding schemes by looking at the ending of the file name.
Some common encodings are csv,
json, xlsx, hdf and so forth.
The path tells us where the data is stored.
Usually, it is stored either on the computer we are using,
or online on the Internet.
In our case, we found a data set of
used cars which was obtained from the web address shown on the slide.
When Jerry entered the web address,
in his web browser, he saw something like this.
Each row is one data point.
A large number of properties are associated with each data point.
Because the properties are separated from each other by commas,
we can guess the data format is csv,
which stands for comma separated values.
At this point, these are just numbers,
and don't mean much to humans.
But once we read in this data,
we can try to make more sense out of it.
In pandas, the read_csv method can read in files with columns,
separated by commas, into a pandas data frame.
Reading data in pandas can be done quickly in three lines.
First import pandas.
Then define a variable with a file path.
And then use the read_csv method to import the data.
However, read_csv assumes the data contains a header.
Our data on used cars has no column headers.
So we need to specify read_csv to not assign headers by setting header to none.
After reading the data set,
it is a good idea to look at the data frame to get a better intuition,
and to ensure that everything occurred the way you expected.
Since printing the entire data set may take up too much time and resources,
to save time, we can just use data frame.head to show the first n rows of the data frame.
Similarly, data frame.tail shows the bottom n rows of data frame.
Here we printed out the first five rows of data.
It seems that the data set was read successfully.
We can see that pandas automatically set the column header as a list of integers.
Because we said header equals none when we read the data.
It is difficult to work with the data frame without having meaningful column names.
However, we can assign column names in pandas.
In our present case, it turned out that we have
the column names in a separate file online.
We first put the column names in a list called headers.
Then, we set df.columns equals
headers to replace the default integer headers by the list.
If we use the head method introduced in the last slide to check the data set,
we see the correct headers inserted at the top of each column.
At some point in time,
after you've done operations on your data frame,
you may want to export your pandas data frame to a new csv file.
You can do this using the method to_csv.
To do this, specify the file path which includes the file name that you want to write to.
For example, if you would like to save the dead data frame
df as automobile.csv to your own computer,
you can use the syntax df.to_csv.
For this course, we will only read and save csv files.
However, pandas also supports importing and
exporting of most data file types with different data set formats.
The code syntax for reading,
and saving other data formats is very similar to read or save csv file.
Each column shows a different method to read and save files into a different format.
